LinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:00.007791200Z",
     "start_time": "2024-03-06T09:51:59.885737100Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   Areas  Prices\n0   1420  220000\n1   1950  310000\n2   2600  420000\n3   1780  290000\n4   2120  350000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Areas</th>\n      <th>Prices</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1420</td>\n      <td>220000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1950</td>\n      <td>310000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2600</td>\n      <td>420000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1780</td>\n      <td>290000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2120</td>\n      <td>350000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= {\"Areas\"\n",
    "        :[1420, 1950, 2600, 1780, 2120, 1350, 2830, 1640, 1240, 2480, \n",
    "                  2060, 1390, 2920, 1750, 2280, 1970, 1340, 2460, 2700, 1550, \n",
    "                  1830, 2240, 1180, 2950, 1630, 2100, 2550, 1440, 1970, 2660, \n",
    "                  2220, 1320, 1880, 1510, 2750, 2160, 1280, 2030, 2490, 2810, \n",
    "                  1580, 1920, 2670, 2290, 1130, 2850, 1520, 2100, 2610],\n",
    "       \"Prices\":[220000, 310000, 420000, 290000, 350000, 200000, 480000, \n",
    "                   270000, 180000, 400000, 320000, 240000, 500000, 260000, \n",
    "                   380000, 330000, 210000, 410000, 460000, 250000, 310000, \n",
    "                   370000, 170000, 490000, 290000, 350000, 430000, 240000, \n",
    "                   320000, 440000, 360000, 190000, 330000, 260000, 450000, \n",
    "                   370000, 200000, 320000, 410000, 480000, 270000, 330000, \n",
    "                   450000, 370000, 160000, 490000, 250000, 350000, 430000]}\n",
    "df=pd.DataFrame(data)\n",
    "df.head(5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:00.096484300Z",
     "start_time": "2024-03-06T09:51:59.915263700Z"
    }
   },
   "id": "8b1c11a02135eeeb",
   "execution_count": 45
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x1bae34c02c0>"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Areas\")\n",
    "plt.ylabel(\"Prices\")\n",
    "plt.scatter(df[\"Areas\"],df.Prices)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:00.623928300Z",
     "start_time": "2024-03-06T09:52:00.070919500Z"
    }
   },
   "id": "59d520f037992823",
   "execution_count": 46
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "LinearRegression()",
      "text/html": "<style>#sk-container-id-4 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-4 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-4 pre {\n  padding: 0;\n}\n\n#sk-container-id-4 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-4 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-4 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-4 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-4 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-4 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-4 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-4 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-4 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-4 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-4 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-4 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-4 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-4 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-4 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-4 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n#sk-container-id-4 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-4 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-4 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-4 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-4 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-4 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-4 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-4 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-4 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-4 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=linear_model.LinearRegression()\n",
    "reg.fit(df[[\"Areas\"]],df.Prices)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:00.675255900Z",
     "start_time": "2024-03-06T09:52:00.481940Z"
    }
   },
   "id": "ee43506706d540c0",
   "execution_count": 47
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\supriya shrivastv\\PycharmProjects\\MachineLearning_Learner\\.venv\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([363938.87948162])"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2200]])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:00.743839300Z",
     "start_time": "2024-03-06T09:52:00.580939800Z"
    }
   },
   "id": "8175d9a9c2475e52",
   "execution_count": 48
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "array([177.47049333])"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef=reg.coef_\n",
    "coef"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:00.747280100Z",
     "start_time": "2024-03-06T09:52:00.599454100Z"
    }
   },
   "id": "45cf0c60340a7b34",
   "execution_count": 49
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "-26496.20583391376"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:00.994392300Z",
     "start_time": "2024-03-06T09:52:00.637203300Z"
    }
   },
   "id": "b48152b650ea2b6a",
   "execution_count": 50
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "array([363938.87948162])"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#y=mx+c\n",
    "coef*2200+reg.intercept_\n",
    "#the value is equal to the valuepref=dicted"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:01.378878800Z",
     "start_time": "2024-03-06T09:52:00.997379900Z"
    }
   },
   "id": "968a49519fb97d3c",
   "execution_count": 51
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x1bae36d83b0>]"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Areas\")\n",
    "plt.ylabel(\"Prices\")\n",
    "plt.scatter(df[\"Areas\"],df.Prices)\n",
    "plt.plot(df[\"Areas\"],reg.predict(df[[\"Areas\"]]),color=\"yellow\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:02.420607800Z",
     "start_time": "2024-03-06T09:52:01.271019700Z"
    }
   },
   "id": "2a302faeb0075ce4",
   "execution_count": 52
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "val={'Areas': [1850, 1950, 2050, 2150, 2250, 2350, 2450, 2550, 2650, 2750,\n",
    "             2850, 2950, 3050, 3150, 3250, 3350, 3450, 3550, 3650, 3750]}\n",
    "test=pd.DataFrame(val)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:02.423527900Z",
     "start_time": "2024-03-06T09:52:02.069929300Z"
    }
   },
   "id": "d66f23717ff19015",
   "execution_count": 53
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   Areas\n0   1850\n1   1950\n2   2050\n3   2150\n4   2250",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Areas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1850</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1950</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2050</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2150</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2250</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:02.537902500Z",
     "start_time": "2024-03-06T09:52:02.275571100Z"
    }
   },
   "id": "cad85287b69dc9ae",
   "execution_count": 54
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "array([301824.20681779, 319571.25615031, 337318.30548284, 355065.35481536,\n       372812.40414789, 390559.45348041, 408306.50281293, 426053.55214546,\n       443800.60147798, 461547.65081051, 479294.70014303, 497041.74947556,\n       514788.79880808, 532535.8481406 , 550282.89747313, 568029.94680565,\n       585776.99613818, 603524.0454707 , 621271.09480323, 639018.14413575])"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:03.109661Z",
     "start_time": "2024-03-06T09:52:02.508725800Z"
    }
   },
   "id": "a44512c2f64c90ed",
   "execution_count": 55
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "test[\"Prices\"]=reg.predict(test[[\"Areas\"]])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:03.338789800Z",
     "start_time": "2024-03-06T09:52:03.109142300Z"
    }
   },
   "id": "33e64c1aa0655b8f",
   "execution_count": 56
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   Areas         Prices\n0   1850  301824.206818\n1   1950  319571.256150\n2   2050  337318.305483\n3   2150  355065.354815\n4   2250  372812.404148",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Areas</th>\n      <th>Prices</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1850</td>\n      <td>301824.206818</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1950</td>\n      <td>319571.256150</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2050</td>\n      <td>337318.305483</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2150</td>\n      <td>355065.354815</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2250</td>\n      <td>372812.404148</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:52:03.534043600Z",
     "start_time": "2024-03-06T09:52:03.273050600Z"
    }
   },
   "id": "1ca0e366abf28941",
   "execution_count": 57
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x1bae7aab9e0>]"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.xlabel(\"Areas\",fontsize=30)\n",
    "plt.ylabel(\"Prices\",fontsize=30)\n",
    "plt.scatter(test[\"Areas\"],test.Prices,color=\"green\")\n",
    "plt.plot(test[\"Areas\"],reg.predict(test[[\"Areas\"]]),color=\"blue\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:57:02.080991700Z",
     "start_time": "2024-03-06T09:57:01.771005300Z"
    }
   },
   "id": "cdd4fe3be34b347a",
   "execution_count": 65
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "276937584884d2d7"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}